Application of aggregation operators to assess the credibility of user-generated content in social media

Nowadays, User-Generated Content (UGC) spreads across social media through Web 2.0 technologies, in the absence of traditional trusted third parties that can verify its credibility. The issue of assessing the credibility of UGC is a recent research topic, which has been tackled by many approaches as a classification problem: information is automatically categorized into genuine and fake, usually by employing data-driven solutions, based on Machine Learning (ML) techniques. In this paper, to address some open issues concerning the use of ML, and to give to the decision maker a major control on the process of UGC credibility assessment, the importance that the Multi-Criteria Decision Making (MCDM) paradigm can have in association with the use of aggregation operators is discussed. Some potential aggregation schemes and their properties are illustrated, as well as some interesting research directions